The Internet is used extensively now by a growing percentage of the public. At this time, several online websites in fact generate the bulk (if not the entirety) of their revenues from servicing online users and subscribers. These include, for example, companies such as AOL and Yahoo! (content providers), Amazon (books, music, and video recordings), EBay (auctions), Netflix (DVD rentals), Google (search engines) and Doubleclick (advertising) to name a few.
All of these companies monitor the interactions of online users with their websites, and in some cases collect explicit profiling information as well from such users. This is done for the purpose of collecting both individualized and aggregate data, which in turn helps them to better customize the site and overall experience for subscribers, to retain subscribers through personalized interactions, to better target advertising and product recommendations, etc. In some instances the data is logged and later used for data mining purposes, such as for identifying trends (a specific example of this is described in U.S. Pat. No. 6,493,703 which is hereby incorporated by reference) and for giving feedback to recommender systems (i.e. such as with Netflix's Cinematch engine).
A similar concept is illustrated in U.S. Patent Publication no. 2003/0004781 to Mallon et al. in which a community “buzz” index can be used to predict popularity, for example, of a particular movie before it is released. This application is also hereby incorporated by reference. Thus, this disclosure specifically mentions the usefulness of monitoring an overall awareness by an online group of certain concepts (i.e., such as the brand name of a product), in order to gauge the potential economic performance of such product later.
A website maintained by Yahoo!—buzz.yahoo.com—(the full URL is not included because of PTO citation restrictions, but can be determined by placing a browser executable suffix) also similarly monitors and tabulates online user content queries/viewings and identifies the same in a so-called “Buzz” score Index that is updated daily and presented for public viewing. This list, in essence, acts as a form of “popularity” identification for certain topics. For example, the list may identify that stories about a particular singer were the most talked about, queried, or viewed.
The Buzz Index by Yahoo! further includes a “Movers” section, which basically identifies people, stories, etc., which experience the greatest degree of change in buzz score on a day to day basis. Thus, for example, a particular celebrity may be identified in a prominent story, and that would elevate such celebrity's “mover” status, even if the overall buzz score was not sufficient to break into the top buzz score index. For further information, the reader is recommended to such website.
Another related system used by Yahool is a marketing tool on another website—solutions.yahoo.com—which permits companies to analyze behavior of online users, and determine particular characteristics which may be useful to such company. For instance, in one case, Yahoo! was able to track online behavior and combine it with traditional demographic and geographic information (to arrive at a subscriber profile) for a company that provided moving services. From this data, they then tried to glean what profiling data was suggestive of a high likelihood of such subscriber moving. In this manner, Yahoo! was able to “mine” the profiles and develop better target advertising for the moving company to a more specific audience. It can be seen that this example can be applied to many other fields.
While the aforementioned Yahoo! systems provide useful information, they fail to yield at least one additional piece of information: namely, which groups or subscribers are “trendsetters.” In other words, while the Yahool Buzz Index identifies the existing top popular concepts, and the concepts which are changing the most at any moment in time, it makes no correlation between the two. That is, from looking at the Buzz Index Score for a particular concept, there is no way for a subscriber to know, which persons or group were the first to be associated with such concept. Similarly, the marketing solutions website is useful for predicting which persons are likely to meet a particular criteria, but does not otherwise identify whether such persons are the first adopters of a particular concept—i.e., such as the first to query/view certain content, the first to buy a particular product, or the first to try a particular service.
This additional piece of information is extremely valuable, because it can be used in a variety of ways to improve an e-commerce website as explained in further detail below.